




"""simple docstring"""







import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


SCREAMING_SNAKE_CASE_	     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	     =       {'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE_	     =       {
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    }
}

SCREAMING_SNAKE_CASE_	     =       {
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

# Segments (not really needed)
SCREAMING_SNAKE_CASE_	     =       0
SCREAMING_SNAKE_CASE_	     =       1
SCREAMING_SNAKE_CASE_	     =       2
SCREAMING_SNAKE_CASE_	     =       3
SCREAMING_SNAKE_CASE_	     =       4

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   A_				      =       VOCAB_FILES_NAMES
   A_				      =       PRETRAINED_VOCAB_FILES_MAP
   A_				      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A_				      =       '''left'''







   def __init__(		self       ,					lowerCamelCase_       ,					lowerCamelCase_=False       ,					lowerCamelCase_=True       ,					lowerCamelCase_=False       ,					lowerCamelCase_="<s>"       ,					lowerCamelCase_="</s>"       ,					lowerCamelCase_="<unk>"       ,					lowerCamelCase_="<sep>"       ,					lowerCamelCase_="<pad>"       ,					lowerCamelCase_="<cls>"       ,					lowerCamelCase_="<mask>"       ,					lowerCamelCase_=["<eop>", "<eod>"]       ,					lowerCamelCase_ = None       ,					**lowerCamelCase_       ,					)				->   None:
       # Mask token behave like a normal word, i.e. include the space before it
       UpperCamelCase              =	AddedToken(lowerCamelCase_       ,					lstrip=lowerCamelCase_       ,					rstrip=lowerCamelCase_) if isinstance(lowerCamelCase_       ,					lowerCamelCase_) else mask_token

       UpperCamelCase              =	{} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           do_lower_case=lowerCamelCase_       ,					remove_space=lowerCamelCase_       ,					keep_accents=lowerCamelCase_       ,					bos_token=lowerCamelCase_       ,					eos_token=lowerCamelCase_       ,					unk_token=lowerCamelCase_       ,					sep_token=lowerCamelCase_       ,					pad_token=lowerCamelCase_       ,					cls_token=lowerCamelCase_       ,					mask_token=lowerCamelCase_       ,					additional_special_tokens=lowerCamelCase_       ,					sp_model_kwargs=self.sp_model_kwargs       ,					**lowerCamelCase_       ,					)

       UpperCamelCase              =	3

       UpperCamelCase              =	do_lower_case
       UpperCamelCase              =	remove_space
       UpperCamelCase              =	keep_accents
       UpperCamelCase              =	vocab_file

       UpperCamelCase              =	spm.SentencePieceProcessor(**self.sp_model_kwargs)
       self.sp_model.Load(lowerCamelCase_)







   @property
   def   UpperCAmelCase__			(		self)				->   List[str]:
       return len(self.sp_model)







   def   UpperCAmelCase__			(		self)				->   Tuple:
       UpperCamelCase              =	{self.convert_ids_to_tokens(lowerCamelCase_): i for i in range(self.vocab_size)}
       vocab.update(self.added_tokens_encoder)
       return vocab







   def __getstate__(		self)				->   Any:
       UpperCamelCase              =	self.__dict__.copy()
       UpperCamelCase              =	None
       return state







   def __setstate__(		self       ,					lowerCamelCase_)				->   str:
       UpperCamelCase              =	d

       # for backward compatibility
       if not hasattr(self       ,					'''sp_model_kwargs'''):
           UpperCamelCase              =	{}

       UpperCamelCase              =	spm.SentencePieceProcessor(**self.sp_model_kwargs)
       self.sp_model.Load(self.vocab_file)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   Union[str, Any]:
       if self.remove_space:
           UpperCamelCase              =	''' '''.join(inputs.strip().split())
       else:
           UpperCamelCase              =	inputs
       UpperCamelCase              =	outputs.replace('''``'''       ,					'''"''').replace('''\'\''''       ,					'''"''')

       if not self.keep_accents:
           UpperCamelCase              =	unicodedata.normalize('''NFKD'''       ,					lowerCamelCase_)
           UpperCamelCase              =	''''''.join([c for c in outputs if not unicodedata.combining(lowerCamelCase_)])
       if self.do_lower_case:
           UpperCamelCase              =	outputs.lower()

       return outputs







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   List[str]:
       UpperCamelCase              =	self.preprocess_text(lowerCamelCase_)
       UpperCamelCase              =	self.sp_model.encode(lowerCamelCase_       ,					out_type=lowerCamelCase_)
       UpperCamelCase              =	[]
       for piece in pieces:
           if len(lowerCamelCase_) > 1 and piece[-1] == str(''',''') and piece[-2].isdigit():
               UpperCamelCase              =	self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCamelCase_       ,					''''''))
               if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                   if len(cur_pieces[0]) == 1:
                       UpperCamelCase              =	cur_pieces[1:]
                   else:
                       UpperCamelCase              =	cur_pieces[0][1:]
               cur_pieces.append(piece[-1])
               new_pieces.extend(lowerCamelCase_)
           else:
               new_pieces.append(lowerCamelCase_)

       return new_pieces







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   int:
       return self.sp_model.PieceToId(lowerCamelCase_)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   Optional[int]:
       return self.sp_model.IdToPiece(lowerCamelCase_)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   Dict:
       UpperCamelCase              =	''''''.join(lowerCamelCase_).replace(lowerCamelCase_       ,					''' ''').strip()
       return out_string







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = False       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = True       ,					**lowerCamelCase_       ,					)				->   str:
       UpperCamelCase              =	kwargs.pop('''use_source_tokenizer'''       ,					lowerCamelCase_)

       UpperCamelCase              =	self.convert_ids_to_tokens(lowerCamelCase_       ,					skip_special_tokens=lowerCamelCase_)

       # To avoid mixing byte-level and unicode for byte-level BPT
       # we need to build string separately for added tokens and byte-level tokens
       # cf. https://github.com/huggingface/transformers/issues/1133
       UpperCamelCase              =	[]
       UpperCamelCase              =	[]
       for token in filtered_tokens:
           if skip_special_tokens and token in self.all_special_ids:
               continue
           if token in self.added_tokens_encoder:
               if current_sub_text:
                   sub_texts.append(self.convert_tokens_to_string(lowerCamelCase_))
                   UpperCamelCase              =	[]
               sub_texts.append(lowerCamelCase_)
           else:
               current_sub_text.append(lowerCamelCase_)
       if current_sub_text:
           sub_texts.append(self.convert_tokens_to_string(lowerCamelCase_))

       # Mimic the behavior of the Rust tokenizer:
       # By default, there are no spaces between special tokens
       UpperCamelCase              =	''''''.join(lowerCamelCase_)

       UpperCamelCase              =	(
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
           UpperCamelCase              =	self.clean_up_tokenization(lowerCamelCase_)
           return clean_text
       else:
           return text







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = None)				->   List[int]:
       UpperCamelCase              =	[self.sep_token_id]
       UpperCamelCase              =	[self.cls_token_id]
       if token_ids_a is None:
           return token_ids_a + sep + cls
       return token_ids_a + sep + token_ids_a + sep + cls







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = None       ,					lowerCamelCase_ = False)				->   List[int]:

       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=lowerCamelCase_       ,					token_ids_a=lowerCamelCase_       ,					already_has_special_tokens=lowerCamelCase_)

       if token_ids_a is not None:
           return ([0] * len(lowerCamelCase_)) + [1] + ([0] * len(lowerCamelCase_)) + [1, 1]
       return ([0] * len(lowerCamelCase_)) + [1, 1]







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = None)				->   List[int]:
       UpperCamelCase              =	[self.sep_token_id]
       UpperCamelCase              =	[2]

       if token_ids_a is None:
           return len(token_ids_a + sep) * [0] + cls_segment_id
       return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_ = None)				->   Tuple[str]:
       if not os.path.isdir(lowerCamelCase_):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory')
           return
       UpperCamelCase              =	os.path.join(
           lowerCamelCase_       ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

       if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCamelCase_) and os.path.isfile(self.vocab_file):
           copyfile(self.vocab_file       ,					lowerCamelCase_)
       elif not os.path.isfile(self.vocab_file):
           with open(lowerCamelCase_       ,					'''wb''') as fi:
               UpperCamelCase              =	self.sp_model.serialized_model_proto()
               fi.write(lowerCamelCase_)

       return (out_vocab_file,)

import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
							lowerCamelCase_    :			int    =	argparse.ArgumentParser()

							parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

							parser.add_argument(
							    """--txt2img_unclip""",
							    default="""kakaobrain/karlo-v1-alpha""",
							    type=str,
							    required=False,
							    help="""The pretrained txt2img unclip.""",
							)

							lowerCamelCase_    :			Dict    =	parser.parse_args()

							lowerCamelCase_    :			str    =	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

							lowerCamelCase_    :			int    =	CLIPImageProcessor()
							lowerCamelCase_    :			Any    =	CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

							lowerCamelCase_    :			Union[str, Any]    =	UnCLIPImageVariationPipeline(
							    decoder=txtaimg.decoder,
							    text_encoder=txtaimg.text_encoder,
							    tokenizer=txtaimg.tokenizer,
							    text_proj=txtaimg.text_proj,
							    feature_extractor=feature_extractor,
							    image_encoder=image_encoder,
							    super_res_first=txtaimg.super_res_first,
							    super_res_last=txtaimg.super_res_last,
							    decoder_scheduler=txtaimg.decoder_scheduler,
							    super_res_scheduler=txtaimg.super_res_scheduler,
							)

							imgaimg.save_pretrained(args.dump_path)






